Objective:To evaluate the effect of automated flexible endoscope channel brushing system (AFECBS) on endoscope reprocessing.Methods:A prospective randomized controlled study was conducted. The used endoscopes were divided into automatic group and manual group by random number table method, 200 in each group. In the automatic group, the AFECBS was used to scrub each tube 3 times during endoscope cleaning; and in the manual group, scrubbing and disinfection personnel routinely brushed each pipeline for 3 times. The primary end point was the qualified rate of endoscopic cleaning quality in the two groups, and the secondary end point was the time spent by the scrubbing and disinfection personnel on the two groups.Results:The qualified rate of overall cleaning in the automatic group was 90.0% (180/200), and in the manual group was 81.0% (162/200). The qualified rate of the automatic group was higher than that of the manual group ( χ2=6.534, P=0.011). The qualified rate of gastroscope cleaning in the automatic group was higher than that in the manual group [92.0% (127/138) VS 81.6% (120/147), χ2=6.658, P=0.010]. There was no significant difference in the qualified rate of colonoscope cleaning between the automatic group and the manual group [85.5% (53/62) VS 79.2% (42/53), χ2=0.774, P=0.379]. When the cleaning personnel scoured 5 endoscopes in each of the two groups, the time of the automatic group (5.17±0.42 min) was shorter than that of the manual group (9.60±0.53 min) ( t=92.644, P<0.001). Conclusion:Compared with manual scrubbing, AFECBS can improve the qualified rate of endoscope cleaning and the work efficiency of scrubbing and disinfection personnel, which is worthy of clinical application.

Objective:To compare the cost-effectiveness before and after using an artificial intelligence gastroscopy-assisted system for early gastric cancer screening.Methods:The gastroscopy cases before (non-AI group) and after (AI group) the use of artificial intelligence gastroscopy-assisted system were retrospectively collected in Renmin Hospital of Wuhan University from January 1, 2017 to February 28, 2022. The proportion of early gastric cancer among all gastric cancer was analyzed. Costs were estimated based on the standards of Renmin Hospital of Wuhan University and the 2021 edition of Wuhan Disease Diagnosis-related Group Payment Standards. Cost-effectiveness analysis was conducted per 100 thousand cases with and without the system. And the incremental cost-effectiveness ratio was calculated.Results:For the non-AI group, the proportion of early gastric cancer among all gastric cancer was 28.81% (70/243). The cost of gastroscopy screening per 100 thousand was 54 598.0 thousand yuan, early gastric treatment cost was 221.8 thousand yuan, and a total cost was 54 819.8 thousand yuan. The direct effectiveness was 894.2 thousand yuan, the indirect effectiveness was 1 828.2 thousand yuan and the total effectiveness was 2 722.4 thousand yuan per 100 thousand cases. For the AI group, the early gastric cancer diagnositic rate was 36.56%(366/1 001), where gastroscopy cost was 53 440.0 thousand yuan, early gastric treatment cost 315.8 thousand yuan, the total cost 53 755.8 thousand yuan. The direct effectiveness was 1 273.5 thousand yuan, indirect effectiveness 2 603.1 thousand yuan and the total effectiveness 3 876.6 thousand yuan per 100 thousand cases. The use of the system reduced the cost of early gastric cancer screening by 1 064.0 thousand yuan, and increased the benefit by 1 154.2 thousand yuan per 100 thousand cases. The incremental cost-effectiveness ratio was -0.92.Conclusion:The use of artificial intelligence gastroscopy-assisted system for gastric early cancer screening can reduce medical costs as well as improve the efficiency of screening, and it is recommended for gastroscopy screening .

Objective:To construct a deep learning-based artificial intelligence endoscopic ultrasound (EUS) bile duct scanning substation system to assist endoscopists in learning multi-station imaging and improve their operation skills.Methods:A total of 522 EUS videos in Renmin Hospital of Wuhan University and Wuhan Union Hospital from May 2016 to October 2020 were collected, and images were captured from these videos, including 3 000 white light images and 31 003 EUS images from Renmin Hospital of Wuhan University, and 799 EUS images from Wuhan Union Hospital. The pictures were divided into training set and test set in the EUS bile duct scanning system. The system included filtering model of white light gastroscopy images (model 1), distinguishing model of standard station images and non-standard station images (model 2) and substation model of EUS bile duct scanning standard images (model 3), which were used to classify the standard images into liver window, stomach window, duodenal bulb window, and duodenal descending window. Then 110 pictures were randomly selected from the test set for a man-machine competition to compare the accuracy of multi-station imaging by experts, advanced endoscopists and the artificial intelligence model.Results:The accuracies of model 1 and model 2 were 100.00% (1 200/1 200) and 93.36% (2 938/3 147) respectively. Those of model 3 on the internal validation dataset in each classification were 97.23% (1 687/1 735) in liver window, 96.89% (1 681/1 735) in stomach window, 98.73% (1 713/1 735) in duodenal bulb window, and 97.18% (1 686/1 735) in duodenal descending window. And those on the external validation dataset were 89.61% (716/799) in liver window, 92.74% (741/799) in stomach window, 90.11% (720/799) in duodenal bulb window, and 92.24% (737/799) in duodenal descending window. In the man-machine competition, the accuracy of the substation model was 89.09% (98/110), which was higher than that of senior endoscopists [85.45% (94/110), 74.55% (82/110), and 85.45% (94/110)] and close to the level of experts [92.73% (102/110) and 90.00% (99/110)].Conclusion:The deep learning-based EUS bile duct scanning system constructed in the current study can assist endoscopists to perform standard multi-station scanning in real time more accurately and improve the completeness and quality of EUS.

Idiopathic pulmonary fibrosis (IPF) is a chronic progressive fibrous interstitial lung disease of unknown etiology. IPF is also considered to be among the independent risk factors for lung cancer, increasing the risk of lung cancer by 7% and 20%. The incidence of IPF complicated with lung cancer, especially non-small cell lung cancer (NSCLC), is increasing gradually, but there is no consensus on unified management and treatment. IPF and NSCLC have similar pathological features. Both appear in the surrounding area of the lung. In pathients with IPF complicated with NSCLC, NSCLC often develops from the honeycomb region of IPF, but the mechanism of NSCLC induced by IPF remains unclear. In addition, IPF and NSCLC have similar genetic, molecular and cellular processes and common signal transduction pathways. The universal signal pathways targeting IPF and NSCLC will become potential therapeutic drugs for IPF complicated with NSCLC. This article examines the main molecular mechanisms involved in IPF and NSCLC and the research progress of drugs under development targeting these signal pathways.
.
Humans ; Carcinoma, Non-Small-Cell Lung/genetics* ; Idiopathic Pulmonary Fibrosis/drug therapy* ; Lung Neoplasms/genetics* ; Lung/pathology* ; Signal Transduction

Objective:To assess the influence of an artificial intelligence (AI) -assisted diagnosis system on the performance of endoscopists in diagnosing gastric cancer by magnifying narrow banding imaging (M-NBI).Methods:M-NBI images of early gastric cancer (EGC) and non-gastric cancer from Renmin Hospital of Wuhan University from March 2017 to January 2020 and public datasets were collected, among which 4 667 images (1 950 images of EGC and 2 717 of non-gastric cancer)were included in the training set and 1 539 images (483 images of EGC and 1 056 of non-gastric cancer) composed a test set. The model was trained using deep learning technique. One hundred M-NBI videos from Beijing Cancer Hospital and Renmin Hospital of Wuhan University between 9 June 2020 and 17 November 2020 were prospectively collected as a video test set, 38 of gastric cancer and 62 of non-gastric cancer. Four endoscopists from four other hospitals participated in the study, diagnosing the video test twice, with and without AI. The influence of the system on endoscopists′ performance was assessed.Results:Without AI assistance, accuracy, sensitivity, and specificity of endoscopists′ diagnosis of gastric cancer were 81.00%±4.30%, 71.05%±9.67%, and 87.10%±10.88%, respectively. With AI assistance, accuracy, sensitivity and specificity of diagnosis were 86.50%±2.06%, 84.87%±11.07%, and 87.50%±4.47%, respectively. Diagnostic accuracy ( P=0.302) and sensitivity ( P=0.180) of endoscopists with AI assistance were improved compared with those without. Accuracy, sensitivity and specificity of AI in identifying gastric cancer in the video test set were 88.00% (88/100), 97.37% (37/38), and 82.26% (51/62), respectively. Sensitivity of AI was higher than that of the average of endoscopists ( P=0.002). Conclusion:AI-assisted diagnosis system is an effective tool to assist diagnosis of gastric cancer in M-NBI, which can improve the diagnostic ability of endoscopists. It can also remind endoscopists of high-risk areas in real time to reduce the probability of missed diagnosis.

Objective:To study the efficacy of different systemic chemotherapy regimens as first-line and second-line therapy and to determine the prognostic factors for patients with advanced biliary tract cancer.Methods:The clinical data of patients with advanced biliary tract cancer who underwent systemic chemotherapy in Cancer Hospital of Chinese Academy of Medical Sciences from January 2011 to December 2018 were studied. The efficacy of chemotherapy on objective response rate (ORR) and disease control rate (DCR) were evaluated. Potential prognostic factors for survival were studied using the Cox proportional hazards models.Results:Of 151 patients enrolled into this study, there were 75 males and 76 females, with ages ranging from 31 to 77 years (median 58 years). Two treatment protocols were used: (1) 104 patients received a gemcitabine-based regimen (combined with platinums or fluorouracils) or a combination of platinums and fluorouracils, while (2) 47 patients received a combination of albumin-bound paclitaxel and S-1. The corresponding ORR for each group were 15.4%(16/104) and 27.6%(13/47), respectively, and the DCR were 65.4%(68/104) and 72.3%(34/47), respectively. Of 58 evaluable patients who received chemotherapy as a second-line therapy, 31 patients received the regimen containing gemcitabine, platinums or fluorouracils with an ORR of 3.2% (1/31) and a DCR of 35.5%(11/31); a total of 18 patients received the taxanes-based regimen with an ORR of 11.1%(2/18) and a DCR of 38.9%(7/18); 9 patients received the irinotecan-based regimen with an ORR of 22.2%(2/9) and a DCR of 44.4%(4/9). Univariate analysis showed positive liver metastasis and elevated carbohydrate antigen (CA)19-9 level to be significantly correlated with worse survival outcomes ( HR=1.540, 95% CI: 1.019-2.328, P=0.040 and HR=1.892, 95% CI: 1.123-3.188, P=0.017). Conclusion:For patients with advanced biliary tract cancer, in addition to the conventional regimens containing gemcitabine, platinums and fluorouracils, the combination of albumin-bound paclitaxel and S-1 was shown to be an effective chemotherapeutic regimen for these patients. Second-line chemotherapy was insufficient and ineffective, and an irinotecan-based regimen deserves to be further investigated. Liver metastasis and elevated CA19-9 level were worse prognosis after chemotherapy for patients with advanced biliary tract cancer.

Objective:To explore the clinicopathological characteristics and prognosis of patients with ovarian metastases from colorectal cancer.Methods:A total of 122 female patients with ovarian metastases from colorectal cancer underwent treatment in Cancer Hospital, Chinese Academy of Medical Sciences between 2010 and 2015 were recruited. The clinicopathological features, treatment details and survival data of these patients were retrospectively analyzed. Kaplan-Maier method was used for survival analysis, log rank test and Cox proportional hazards model were used for prognostic factor analysis.Results:The median overall survival (OS) was 19.7 months. The 1-year, 3-years and 5-years OS rates were 72.1%, 24.7% and 9.9%, respectively. A total of 99 (81.1%) patients underwent oophorectomy. The median OS of patients who underwent oophorectomy was 21.9 months, significantly longer than 10.3 months of patients without oophorectomy ( P<0.01). Ovary as the only site of metastasis, primary tumor resection, and oophorectomy were associated with improved survival (all P<0.01). Primary tumor resection and oophorectomy were independent prognostic factors for OS (both P<0.01). Conclusion:Patients with ovarian metastases from colorectal cancer might acquire a survival benefit from surgical resection of the primary tumor and ovaries.

Objective:To explore the clinicopathological characteristics and prognosis of patients with ovarian metastases from colorectal cancer.Methods:A total of 122 female patients with ovarian metastases from colorectal cancer underwent treatment in Cancer Hospital, Chinese Academy of Medical Sciences between 2010 and 2015 were recruited. The clinicopathological features, treatment details and survival data of these patients were retrospectively analyzed. Kaplan-Maier method was used for survival analysis, log rank test and Cox proportional hazards model were used for prognostic factor analysis.Results:The median overall survival (OS) was 19.7 months. The 1-year, 3-years and 5-years OS rates were 72.1%, 24.7% and 9.9%, respectively. A total of 99 (81.1%) patients underwent oophorectomy. The median OS of patients who underwent oophorectomy was 21.9 months, significantly longer than 10.3 months of patients without oophorectomy ( P<0.01). Ovary as the only site of metastasis, primary tumor resection, and oophorectomy were associated with improved survival (all P<0.01). Primary tumor resection and oophorectomy were independent prognostic factors for OS (both P<0.01). Conclusion:Patients with ovarian metastases from colorectal cancer might acquire a survival benefit from surgical resection of the primary tumor and ovaries.

Objective:To construct an artificial intelligence-assisted diagnosis system to detect gastric ulcer lesions and identify benign and malignant gastric ulcers automatically.Methods:A total of 1 885 endoscopy images were collected from November 2016 to April 2019 in the Digestive Endoscopy Center of Renmin Hospital of Wuhan University. Among them, 636 were normal images, 630 were with benign gastric ulcers, and 619 were with malignant gastric ulcers. A total of 1 735 images belonged to training data set and 150 images were used for validation. These images were input into the Res-net50 model based on the fastai framework, the Res-net50 model based on the Keras framework, and the VGG-16 model based on the Keras framework respectively. Three separate binary classification models of normal gastric mucosa and benign ulcers, normal gastric mucosa and malignant ulcers, and benign and malignant ulcers were constructed.Results:The VGG-16 model showed the best ability of classification. The accuracy of the validation set was 98.0%, 98.0% and 85.0%, respectively, for distinguishing normal gastric mucosa from benign ulcers, normal gastric mucosa from malignant ulcers, and benign ulcers from malignant ulcers.Conclusion:The artificial intelligence-assisted diagnosis system obtained in this study shows noteworthy ability of detection of ulcerous lesions, and is expected to be used in clinical to assist doctors to detect ulcer and identify benign and malignant ulcers.